But knowing that doesn't necessarily simplify the task of hiring qualified data scientists. Booz Allen Hamilton's "The Field Guide to Data Science" is a 110-page primer that goes into great detail on how to build a data science team. We've summarized some salient details below.

When building your team, it's important to focus on these key qualities:

Curiosity: Required to "peel apart" problems and study relationships between data, including those that may at first glance seem unrelated.

Creativity: For devising and attempting new problem-solving approaches and potential solutions that haven't been tried before.

Focus: Essential for designing and testing techniques over lengthy periods (days or weeks). A tenacious attitude is important for learning from failure and trying until you get it right.

Attention to detail: Important for maintaining rigor and for avoiding an over-reliance on intuition when analyzing data.

A computer science background is essential for data processing and manipulation. Advanced math skills, including a solid background in calculus, geometry, linear algebra, and statistics, are required for understanding the basis for algorithms and other data science tools, the report said. And domain expertise helps the data scientist understand the problem at hand and how to measure it.

Where do you find data scientists? Your first impulse may be to look outside of your organization for qualified candidates, but the Field Guide recommends first looking in-house for people "who have a high aptitude" for data science.

Potential team members will likely have advanced degrees in the three technical areas described above, but you shouldn't immediately dismiss candidates who don't.

"Don't discount anyone -- you will find data scientists in the strangest places with the oddest combinations of backgrounds," the report states.

Leadership skills are important, particularly for the first members chosen for your team.

One common weakness of data science teams is an inability or unwillingness to imagine new and different approaches to problems. You'll want to foster an environment of openness, one that encourages "trust and communication across all levels, instead of deference to authority." Managers should encourage data science team members to speak up and ask questions frequently.

In addition to building the team, you'll need to choose an operating model. The Field Guide suggests three options:

A centralized team that works under a chief data scientist and serves the analytical needs of the entire organization

Political, not technical, problems are often the biggest challenges facing a data science unit, particularly if management is ambivalent about the team's mission.

To prove its value, a team "needs to initially focus on the hardest problems within an organization that have the highest return for key stakeholders." Doing so can change (ideally for the better) how the organization approaches future challenges.

A data science team also needs support from management to lessen in-house "fears and doubts" about its mission. Leaders must be strong advocates to ensure "widespread buy-in" of the team's objectives, the report says.

Jeff Bertolucci is a technology journalist in Los Angeles who writes mostly for Kiplinger's Personal Finance, the Saturday Evening Post, and InformationWeek.

You can use distributed databases without putting your company's crown jewels at risk. Here's how. Also in the Data Scatter issue of InformationWeek: A wild-card team member with a different skillset can help provide an outside perspective that might turn big data into business innovation. (Free registration required.)

It's an interesting point that inventors work better alone but it's worth looking at the work that data scientists are doing and whether they would be better suited working with others. While a team-first dynamic can often hinder a person's ability to innovate and be creative, the ability to loop back with teammates after creating a product or model, can be the trigger that leads others to an innovation. Here at Alpine Data Labs our product allows data scientists to share their thoughts with teammates to build toward the best possible solution.

Most inventors and engineers I've met are like me – they're shy and they live in their heads. They're almost like artists. In fact, the very best of them are artists. And artists work best alone where they can control an invention's design without a lot of other people designing it for marketing or some other committee. I don't believe anything really revolutionary has been invented by committee. If you're that rare engineer who's an inventor and also an artist, I'm going to give you some advice that might be hard to take. That advice is: Work alone. You're going to be best able to design revolutionary products and features if you're working on your own. Not on a committee. Not on a team.

Given the scarcity of analytics talent, there seems to be broad agreement that you have to structure teams in a centralized way to share the talent. That said, having teams move from project to project or come up with some sort of hybrid model can also spread the wealth. Facebook embeds analytics talent in each business unit, but these people also report to a centralized head of analytics so they can share good ideas and lessons learned and avoid overlapping projects.

Most IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.

Why should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.